Abstract A brain connectome at the macroscale is typically represented as networks, where nodes are brain regions of interest (ROIs) and links indicate their functional or structural connections. Both functional and structural brain network architecture are heritable and found disrupted in AD or its prodromal stage. Recent availability of brain-wide transcriptome data has made possible another type of brain connectome, brain co-expression network, which captures spatial variations in gene expression with links as transcriptional coupling between ROIs. Some studies showed that co-expression network is closely connected to structural and functional brain networks. However, the genes inducing such connection remains unknown. Identification of these genes will transform our understanding of the biological underpinnings of altered neural system in AD and can exert a huge impact on the development of new diagnostic, therapeutic and preventative approaches for AD. The complexity of network data, however, has presented critical computational challenge requiring new concepts and enabling approaches. To address these challenges, we propose novel integrative approaches and perform the following two tasks: 1) identifying functional and structural brain networks altered in AD via meta-analyses, and 2) identifying the genes underlying the association between co- expression networks and AD-altered networks. By leveraging the brain-wide transcriptome data, we will learn a small set of genes whose co-expression patterns across ROIs can best explain their altered connections in AD. If successful, results from this project will transform our understanding of the interplay between genes and brain regions in AD, and thus be expected to impact biomedical research in general and benefit public health outcomes.